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PROJECT TITLE :

Trace-driven analysis for location-dependent pricing in mobile cellular networks

ABSTRACT:

Due to increasingly severe mobile cellular network congestion, particularly throughout peak hours within the urban area, dynamic sensible knowledge pricing mechanisms are proposed for service providers to shift users' data consumption from peak hours to off-peak periods. Time-dependent pricing, as a major kind of existing sensible information pricing mechanism, fails to utilize location info and may perform poorly with spatially heterogeneous mobile traffic. In this text, we have a tendency to perform a trace-driven analysis on the motivation and benefit of location-dependent pricing, primarily based on a massive-scale cellular network dataset as well as 9000 base stations and three,500,000 subscribers. Our trace-driven analysis finds that location is another necessary factor that affects the consumption of mobile data, and also the spatially heterogeneous feature should be thought of in the design of pricing methods. Through quantitative analysis, we reveal that even intuitively using location data in information pricing can considerably enhance system performance, reducing the peak-to-average ratio of traffic consumption by a lot of than fifteen % in [*fr1] of the base stations compared to pure time-dependent pricing.


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